Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77100
Title: GNSS multipath detection using a machine learning approach
Authors: Hsu, LT 
Keywords: Global Positioning System
Machine Learning
Multipath
NLOS
Support Vector Machine
Urban Area
Issue Date: 2017
Source: 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017, Yokohama, Japan, 16-19 October 2017 How to cite?
Abstract: Insufficient localization accuracy of global navigation satellite system (GNSS) receivers is one of the challenges to implement advanced intelligent transportation system in highly urbanized areas. Multipath and non-line-of-sight (NLOS) effects strongly deteriorate GNSS positioning performance. This paper aims to train a classifier by supervised machine learning to separate the type of GNSS pseudorange measurement into three categories, clean, multipath and NLOS. Several features obtained or calculated from the GNSS raw data are evaluated. This paper also proposes a new feature to indicate the consistency between measurements of pseudorange and Doppler shift. According to the experiment result, about 75% of classification accuracy can be achieved using a support vector machine (SVM) classifier trained by the proposed feature and received signal strength.
URI: http://hdl.handle.net/10397/77100
ISBN: 978-1-5386-1526-3
DOI: 10.1109/ITSC.2017.8317700
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Hsu, L. T. (2017, October). GNSS multipath detection using a machine learning approach. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on (pp. 1-6). IEEE. is available at https://doi.org/10.1109/ITSC.2017.8317700
Appears in Collections:Conference Paper

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